Traditional methods of increasing the apparent sharpness of a digital image, such as the technique of unsharp masking, often produce unwanted artifacts at large transition edges in the image. For example, unsharp masking is often described by the equation:Sproc=Sorg+B(Sorg−Sus) where Sproc represents the processed image signal in which the high frequency components have been amplified, Sorg represents the original image signal, Sus represents the unsharp image signal, typically a smoothed image signal obtained by filtering the original image, and B represents the high frequency emphasis coefficient.
The unsharp masking operation may be modeled as a linear system. Thus, the magnitude of any frequency in Sproc is directly dependent upon the magnitude of that frequency in the Sorg image signal. As a consequence of this superposition principle, large edges in the Sorg image signal will often display a ringing artifact in the Sproc signal when the desired level of high frequency enhancement has been performed in other areas of the Sproc signal. This ringing artifact appears as a light or dark outline around the large edge, and may be visually objectionable.
Many non-linear filters based on local statistics exist for the purposes of noise reduction, sharpening, and contrast adjustment. For example, the median filter is well known in the art. In this filter, typically implemented for noise reduction, each pixel is replaced with the median value of some surrounding neighborhood. This filtering process is generally very successful at removing impulse noise; however, the processed image appears slightly less sharp than the original.
Another example of a non-linear filter based on local statistics is local histogram equalization, referred to as adaptive histogram modification by William Pratt on pages 278-284 of the book Digital Image Processing, Second Edition, John Wiley & Sons, 1991. With this filter, the values of pixels are modified by the cumulative histogram of a local window. This technique effectively adjusts the contrast of each region of a digital image, effectively increasing the local contrast in some regions of the image, and decreasing the contrast in other regions. This technique does not intend to increase the apparent sharpness of any given region. Also, this technique does not ensure that the typical artifacts of ringing will not occur.
There exist many algorithms for sharpening the appearance of images without generating artifacts or enhancing the notability of noise. In U.S. Pat. No. 4,571,635, Mahmoodi and Nelson teach the method of deriving an emphasis coefficient B that is used to scale the high frequency information of the digital image depending upon the standard deviation of image pixel values in a local neighborhood. In addition, in U.S. Pat. No. 5,081,692, Kwon and Liang teach that the emphasis coefficient B is based upon a center weighted variance calculation. However, neither Mahmoodi et al nor Kwon et al consider the expected standard deviation of noise inherent in the imaging system. By not considering the noise inherent in the imaging system, both Mahmoodi and Kwon make the implicit assumption that all imaging sources and intensities have the same noise characteristics. In addition, neither use separate strategies for texture and edge regions.
In U.S. Pat. No. 4,794,531, Morishita et al teaches a method of generating the unsharp image with a filter whose weights on neighboring pixels are based upon the absolute difference between the central pixel and the neighboring pixel. Morishita claims that this method effectively reduces artifacts seen at the edges of a sharpened image (as compared with traditional unsharp masking). In addition, Morishita sets a gain parameter based upon local standard deviation and the standard deviation of the entire image. Again, Morishita does not consider the levels of noise inherent in the imaging system in order to approximate signal to noise ratios. In addition, the method of Morishita does not offer explicit control over the amount of edge reshaping.
In U.S. Pat. No. 5,038,388, Song teaches a method of amplifying image details without amplifying the image noise by adaptively amplifying the high-frequency component of the image. An estimate of image noise power is used, however; this noise power is not described as being dependent on the intensity or the pixel. In addition, Song does not attempt to estimate signal to noise ratio in order to control the level of sharpening.
In U.S. Pat. No. 4,689,666 Hatanaka discloses a method of using the in color characteristics of a color digital image for the purposes of reducing the noise component of the color digital image. Hatanaka describes a process of extracting color data for each picture element of the image, discriminating regions of the color digital image exhibiting a specific color on the basis of the extracted color data, and subjecting the image signal to spatial image processing for elimination of noise under different processing conditions for regions exhibiting the specific color and the remaining regions not exhibiting the specific color. Thus the method taught by Hatanaka has as a fundamental step the segmentation, or discrimination, of each pixel as belonging to the specific color or not belonging to the specific color. The step color discrimination can lead to unevenness the processed images due to the on/off nature of the color identification process.
In U.S. Pat. No. 5,682,443 Gouch and MacDonald disclose a method of processing color digital images for the purpose of spatial sharpness characteristic enhancement. A method of unsharp masking is described which separates each color channel of the original color digital image into two parts based solely on the spatial frequency content of the original color channel. The difference between the original color channel and a low spatial frequency component of the original color channel forms a fringe component, or high spatial frequency component of the original color channel. Gouch and MacDonald teach a method of modifying the fringe component based on the color of either the low spatial frequency component or the original pixel values. The color considered is derived from the identical color channels that are sharpened. Their patent also discloses that the preferred method of implementing this feature uses a continuous mathematical function of color. The method disclosed by Gouch and MacDonald takes advantage of color as an image characteristic for enhancing the spatial detail. However, the unsharp masking procedure employed by Gouch and MacDonald has several shortcomings. First, their method only considers cases where each color channel of a color digital image undergoes the described unsharp masking operation. The variable weight used to modify the fringe data is derived from all of the color channels. Their method fails to consider cases when only a single digital image channel from a color digital image is sharpened, as is often the case when an image is represented as a luminance channel and a collection of chrominance channels. Additionally, Gouch and MacDonald necessitate that the modified fringe data is combined with the original pixel data. However, in many applications, it is preferred to modify the signal to which the modified fringe data is added. Thus, their method fails to consider cases where it is desirable to modify the signal to which the modified fringe data is combined.
Thus, there exists a need for an alternative method of manipulating a digital image in order to generate an image signal that appears to be sharper, or more in focus, while minimizing the ringing artifact that is evident with the unsharp masking technique and enhancing the magnitude of detail in the scene in a noise sensitive manner.